Hi all!
I am having a problem with the fmin_bfgs solver that's surprising to me.
Here's the toy problem I've set up:
from scipy.optimize import fmin_bfgs, fmin_ncg
from numpy import *
import numpy as np
def f(x ):
if x < 0:
return 1.79769313e+308
else :
return x + 1./x
xs = fmin_bfgs(f, array( [10.]), retall = True)
The solver returns [nan] as the solution.
The problem is designed to be stiff: between 0 and 1, it slopes upward to
infinity but between 1 and infinity, it slopes up at a slope of 1. Left of
0 the function has a "nearly infinite" value. If bfgs encounters a value
that's larger than the current value, it should try a different step size,
no? Why does fmin_bfgs fail in this way?
Cheers,
John
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